{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Importing Libraries","metadata":{}},{"cell_type":"code","source":"! pip install biosppy   ","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:06:49.032837Z","iopub.execute_input":"2021-08-02T18:06:49.033199Z","iopub.status.idle":"2021-08-02T18:07:01.503610Z","shell.execute_reply.started":"2021-08-02T18:06:49.033169Z","shell.execute_reply":"2021-08-02T18:07:01.502362Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting biosppy\n  Downloading biosppy-0.7.3.tar.gz (85 kB)\n\u001b[K     |████████████████████████████████| 85 kB 1.4 MB/s eta 0:00:011\n\u001b[?25hCollecting bidict\n  Downloading bidict-0.21.2-py2.py3-none-any.whl (37 kB)\nRequirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from biosppy) (2.10.0)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from biosppy) (3.4.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.19.5)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from biosppy) (0.23.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.6.3)\nRequirement already satisfied: shortuuid in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.0.1)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.15.0)\nRequirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.0.1)\nRequirement already satisfied: opencv-python in /opt/conda/lib/python3.7/site-packages (from biosppy) (4.5.2.54)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (8.2.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (0.10.0)\nRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (2.4.7)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (1.3.1)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (2.8.1)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->biosppy) (2.1.0)\nBuilding wheels for collected packages: biosppy\n  Building wheel for biosppy (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for biosppy: filename=biosppy-0.7.3-py2.py3-none-any.whl size=95409 sha256=74f20e6914b8eafd929347ef37b53c25cf6304b8652a00bcf0695cd19ec47270\n  Stored in directory: /root/.cache/pip/wheels/2f/4f/8f/28b2adc462d7e37245507324f4817ce1c64ef2464f099f4f0b\nSuccessfully built biosppy\nInstalling collected packages: bidict, biosppy\nSuccessfully installed bidict-0.21.2 biosppy-0.7.3\n\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport wfdb\nimport os                                                                                                  \nimport gc\nimport scipy       \nimport sklearn\nfrom pathlib import Path\nfrom sklearn.utils import shuffle\nfrom sklearn.manifold import TSNE\nimport seaborn as sns\nfrom sklearn import preprocessing\nimport shutil\nimport math\nimport random\nfrom scipy.spatial import distance\nfrom biosppy.signals import ecg\nfrom scipy.interpolate import PchipInterpolator","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:07:01.505487Z","iopub.execute_input":"2021-08-02T18:07:01.505852Z","iopub.status.idle":"2021-08-02T18:07:01.622358Z","shell.execute_reply.started":"2021-08-02T18:07:01.505813Z","shell.execute_reply":"2021-08-02T18:07:01.621360Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection\n    print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\nexcept ValueError:\n    raise BaseException('ERROR: Not connected to a TPU runtime; please see the previous cell in this notebook for instructions!')\n\ntf.config.experimental_connect_to_cluster(tpu)\ntf.tpu.experimental.initialize_tpu_system(tpu)\ntpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:07:02.258955Z","iopub.execute_input":"2021-08-02T18:07:02.259307Z","iopub.status.idle":"2021-08-02T18:07:08.316862Z","shell.execute_reply.started":"2021-08-02T18:07:02.259277Z","shell.execute_reply":"2021-08-02T18:07:08.315960Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Running on TPU  ['10.0.0.2:8470']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Dataset Creation","metadata":{}},{"cell_type":"markdown","source":"## MIT-BIH Dataset","metadata":{}},{"cell_type":"code","source":"####### Dataset Creation  \n###### Reading Dataset\n\n##### Function to read a record from single dataset\ndef data_read_mit(filepath):\n    \n    \"\"\"\n    Function to read the the inputs from MIT-BIH Dataset\n    INPUTS:- \n    1)filepath: Path to the csv file\n    \n    OUTPUTS:-\n    1)Output_signal: Output 1D array signal \n    \"\"\"\n    return (np.array(pd.read_csv(filepath,index_col=0).iloc[:,[0]]))   \n    \n##### Function to Required Annotations from .txt files\ndef feature_extractor(txt_file_path):\n    \n    \"\"\"\n       Function to extract time series data \n       from a .txt file\n    \"\"\"\n    \n    #### Reading File\n    line_list = []\n\n    with open(txt_file_path, 'r') as reader:\n\n    # Read and print the entire file line by line\n        line = reader.readline()\n        while line != '':  # The EOF char is an empty string\n            #print(line, end='')\n            line_list.append(line)\n            line = reader.readline()\n    \n    #### Taking the Time Step Data\n    line_list = line_list[1:]\n    \n    #### Splitting the Collected Text Strings and Converting them into Floating type values\n    new_list = []\n\n    for item in line_list:\n        for idx,sub_item in enumerate(item.split()):\n            if(idx == 1):\n                new_list.append(int(sub_item))\n                break\n    \n    ### Returning the feature extracted list as numpy array\n    return np.array(new_list)\n\n###### Function to Segment Signals\n##### Constants\nFS = 500\nW_LEN = 256\nW_LEN_1_4 = 256 // 4\nW_LEN_3_4 = 3 * (256 // 4)\n\n##### Function\ndef segmentSignals(signal, r_peaks_annot, normalization=True, person_id= None, file_id=None):\n    \n    \"\"\"\n    Segments signals based on the detected R-Peak\n    Args:\n        signal (numpy array): input signal\n        r_peaks_annot (int []): r-peak locations.\n        normalization (bool, optional): apply z-normalization or not? . Defaults to True.\n        person_id ([type], optional): [description]. Defaults to None.\n        file_id ([type], optional): [description]. Defaults to None.\n    Returns:\n            [tuple(numpy array,numpy array)]: segmented signals and refined r-peaks\n    \"\"\"\n    def refine_rpeaks(signal, r_peaks):\n        \"\"\"\n        Refines the detected R-peaks. If the R-peak is slightly shifted, this assigns the \n        highest point R-peak.\n        Args:\n            signal (numpy array): input signal\n            r_peaks (int []): list of detected r-peaks\n        Returns:\n            [numpy array]: refined r-peaks\n        \"\"\"\n\n        r_peaks2 = np.array(r_peaks)            # make a copy\n        for i in range(len(r_peaks)):\n            r = r_peaks[i]          # current R-peak\n            small_segment = signal[max(0,r-100):min(len(signal),r+100)]         # consider the neighboring segment of R-peak\n            r_peaks2[i] = np.argmax(small_segment) - 100 + r_peaks[i]           # picking the highest point\n            r_peaks2[i] = min(r_peaks2[i],len(signal))                          # the detected R-peak shouldn't be outside the signal\n            r_peaks2[i] = max(r_peaks2[i],0)                                    # checking if it goes before zero    \n        return r_peaks2                     # returning the refined r-peak list\n    \n    segmented_signals = []                      # array containing the segmented beats\n    \n    r_peaks = np.array(r_peaks_annot)\n    r_peaks = refine_rpeaks(signal, r_peaks)\n    skip_len = 5 # Parameter to specify number of r_peaks in one signal\n    max_seq_len = 1280 # Parameter to specify maximum sequence length\n    \n    for r_curr in range(0,int(r_peaks.shape[0]-(skip_len-1)),skip_len):\n        if ((r_peaks[r_curr]-W_LEN_1_4)<0) or ((r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4)>=len(signal)):           # not enough signal to segment\n            continue\n        segmented_signal = np.array(signal[r_peaks[r_curr]-W_LEN_1_4:r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4])        # segmenting a heartbeat\n        segmented_signal = list(segmented_signal)\n        #print(segmented_signal.shape)\n        \n        if(len(segmented_signal) < 1280):\n            for m in range(int(1280-len(segmented_signal))): # Zero Padding\n                segmented_signal.append(0)\n        else:\n            segmented_signal = (segmented_signal[:int(max_seq_len)])\n            \n        segmented_signal = np.array(segmented_signal)\n        \n        if(segmented_signal.shape != (1280,1)):    \n            segmented_signal = np.reshape(segmented_signal,(1280,1))\n            \n        if (normalization):             # Z-score normalization\n            if abs(np.std(segmented_signal))<1e-6:          # flat line ECG, will cause zero division error\n                continue\n            segmented_signal = (segmented_signal - np.mean(segmented_signal)) / np.std(segmented_signal)            \n              \n        #if not np.isnan(segmented_signal).any():                    # checking for nan, this will never happen\n            segmented_signals.append(segmented_signal)\n\n    return segmented_signals,r_peaks           # returning the segmented signals and the refined r-peaks","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:07:14.679991Z","iopub.execute_input":"2021-08-02T18:07:14.680654Z","iopub.status.idle":"2021-08-02T18:07:14.702737Z","shell.execute_reply.started":"2021-08-02T18:07:14.680615Z","shell.execute_reply":"2021-08-02T18:07:14.701688Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"###### Looping for Reading the Entire Dataset - OpenSet Testing\n\n! mkdir './5_Beat_Ecg_MITBIH_1' # Generating major database of datasets\ncurrent_index = 1\n\nmit_dbs_path = '../input/mitbih-database'\nfor i in range(0,96,2): # Loop over all the files\n    print(i)\n    print(np.sort(os.listdir(mit_dbs_path))[i])\n    rec_file_path = os.path.join(mit_dbs_path,str(np.sort(os.listdir(mit_dbs_path))[i])) # Path Selection\n    attr_file_path = os.path.join(mit_dbs_path,str(np.sort(os.listdir(mit_dbs_path))[i+1])) # Path Selction\n    \n    signal_current = data_read_mit(rec_file_path) # Current ECG Signal\n    r_peaks_current = feature_extractor(attr_file_path) # R-peaks\n    seg_signal_current,new_r_peaks = (segmentSignals(signal_current,list(r_peaks_current))) # Segmented ECG Signals\n    #seg_signal_current = np.array(seg_signal_current)\n    #print(seg_signal_current.shape[0])\n    \n    if(i == 48):\n        current_index = current_index-1\n        current_storage_path = './5_Beat_Ecg_MITBIH_1'+'/person'+str(current_index)\n        #Path(current_storage_path).mkdir(parents=True, exist_ok=True)\n    \n        for j in range(len(seg_signal_current)):\n            file_name_current = current_storage_path+'/Extra'+str(j)\n            np.savez_compressed(file_name_current,seg_signal_current[j])\n            \n        current_index = current_index+1\n    \n    else:\n        current_storage_path = './5_Beat_Ecg_MITBIH_1'+'/person'+str(current_index)\n        Path(current_storage_path).mkdir(parents=True, exist_ok=True)\n    \n        for j in range(len(seg_signal_current)):\n            file_name_current = current_storage_path+'/'+str(j)\n            np.savez_compressed(file_name_current,seg_signal_current[j])\n            \n        current_index = current_index+1","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:07:44.068280Z","iopub.execute_input":"2021-08-02T18:07:44.069043Z","iopub.status.idle":"2021-08-02T18:13:03.413870Z","shell.execute_reply.started":"2021-08-02T18:07:44.069001Z","shell.execute_reply":"2021-08-02T18:13:03.412621Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"0\n100.csv\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:113: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n","output_type":"stream"},{"name":"stdout","text":"2\n101.csv\n4\n102.csv\n6\n103.csv\n8\n104.csv\n10\n105.csv\n12\n106.csv\n14\n107.csv\n16\n108.csv\n18\n109.csv\n20\n111.csv\n22\n112.csv\n24\n113.csv\n26\n114.csv\n28\n115.csv\n30\n116.csv\n32\n117.csv\n34\n118.csv\n36\n119.csv\n38\n121.csv\n40\n122.csv\n42\n123.csv\n44\n124.csv\n46\n200.csv\n48\n201.csv\n50\n202.csv\n52\n203.csv\n54\n205.csv\n56\n207.csv\n58\n208.csv\n60\n209.csv\n62\n210.csv\n64\n212.csv\n66\n213.csv\n68\n214.csv\n70\n215.csv\n72\n217.csv\n74\n219.csv\n76\n220.csv\n78\n221.csv\n80\n222.csv\n82\n223.csv\n84\n228.csv\n86\n230.csv\n88\n231.csv\n90\n232.csv\n92\n233.csv\n94\n234.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"###### Creation of Numpy Arrays\n##### Defining Essentials\ndata_folder = './5_Beat_Ecg_MITBIH_1'\nX_train = []\nX_dev = []\ny_train = []     \ny_dev = []\n\n##### Looping Over to populate the dataset\nfor index,sub_data_folder in enumerate(np.sort(os.listdir(data_folder))):\n    sub_data_folder_path = os.path.join(data_folder,sub_data_folder)\n    \n    #if(index <= 33):\n        \n    for idx,item in enumerate(np.sort(os.listdir(sub_data_folder_path))): # Looping Over a person's folder\n        item_path = os.path.join(sub_data_folder_path,item)\n\n        #### Train on Past Test on Present (TPTP)\n        if(idx <= int(np.round(len(os.listdir(sub_data_folder_path))*0.7))):\n            X_train.append(np.load(item_path,allow_pickle=True)['arr_0'])\n            y_train.append(index)\n        else:\n            X_dev.append(np.load(item_path,allow_pickle=True)['arr_0'])\n            y_dev.append(index)\n            \n    print('Person'+' '+str(index+1)+'s data taken')\n    \n##### Creation of Numpy Arrays\nX_train = np.array(X_train)\nX_dev = np.array(X_dev)\ny_train = np.array(y_train)\ny_dev = np.array(y_dev)\n\nprint(np.max(y_train))\nprint(np.min(y_train))\n\n##### Shuffling Dataset\nX_train,y_train = shuffle(X_train,y_train,random_state=23)\nX_dev,y_dev = shuffle(X_dev,y_dev,random_state=23)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:15:07.068328Z","iopub.execute_input":"2021-08-02T18:15:07.068722Z","iopub.status.idle":"2021-08-02T18:16:13.632526Z","shell.execute_reply.started":"2021-08-02T18:15:07.068674Z","shell.execute_reply":"2021-08-02T18:16:13.631381Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Person 1s data taken\nPerson 2s data taken\nPerson 3s data taken\nPerson 4s data taken\nPerson 5s data taken\nPerson 6s data taken\nPerson 7s data taken\nPerson 8s data taken\nPerson 9s data taken\nPerson 10s data taken\nPerson 11s data taken\nPerson 12s data taken\nPerson 13s data taken\nPerson 14s data taken\nPerson 15s data taken\nPerson 16s data taken\nPerson 17s data taken\nPerson 18s data taken\nPerson 19s data taken\nPerson 20s data taken\nPerson 21s data taken\nPerson 22s data taken\nPerson 23s data taken\nPerson 24s data taken\nPerson 25s data taken\nPerson 26s data taken\nPerson 27s data taken\nPerson 28s data taken\nPerson 29s data taken\nPerson 30s data taken\nPerson 31s data taken\nPerson 32s data taken\nPerson 33s data taken\nPerson 34s data taken\nPerson 35s data taken\nPerson 36s data taken\nPerson 37s data taken\nPerson 38s data taken\nPerson 39s data taken\nPerson 40s data taken\nPerson 41s data taken\nPerson 42s data taken\nPerson 43s data taken\nPerson 44s data taken\nPerson 45s data taken\nPerson 46s data taken\nPerson 47s data taken\n46\n0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## PTB Dataset","metadata":{}},{"cell_type":"code","source":"###### Constants\nFS = 360\nW_LEN = 256\nW_LEN_1_4 = 256 // 4\nW_LEN_3_4 = 3 * (256 // 4)\n\n###### Function to Segment Signals\n\ndef segmentSignals(signal, r_peaks_annot, normalization=True, person_id= None, file_id=None):\n    \n    \"\"\"\n    Segments signals based on the detected R-Peak\n    Args:\n        signal (numpy array): input signal\n        r_peaks_annot (int []): r-peak locations.\n        normalization (bool, optional): apply z-normalization or not? . Defaults to True.\n        person_id ([type], optional): [description]. Defaults to None.\n        file_id ([type], optional): [description]. Defaults to None.\n    Returns:\n            [tuple(numpy array,numpy array)]: segmented signals and refined r-peaks\n    \"\"\"\n    def refine_rpeaks(signal, r_peaks):\n        \"\"\"\n        Refines the detected R-peaks. If the R-peak is slightly shifted, this assigns the \n        highest point R-peak.\n        Args:\n            signal (numpy array): input signal\n            r_peaks (int []): list of detected r-peaks\n        Returns:\n            [numpy array]: refined r-peaks\n        \"\"\"\n        r_peaks2 = np.array(r_peaks)            # make a copy\n        for i in range(len(r_peaks)):\n            r = r_peaks[i]          # current R-peak\n            small_segment = signal[max(0,r-100):min(len(signal),r+100)]         # consider the neighboring segment of R-peak\n            r_peaks2[i] = np.argmax(small_segment) - 100 + r_peaks[i]           # picking the highest point\n            r_peaks2[i] = min(r_peaks2[i],len(signal))                          # the detected R-peak shouldn't be outside the signal\n            r_peaks2[i] = max(r_peaks2[i],0)                                    # checking if it goes before zero    \n        return r_peaks2                     # returning the refined r-peak list\n    \n    segmented_signals = []                      # array containing the segmented beats\n    \n    r_peaks = np.array(r_peaks_annot)\n\n    r_peaks = refine_rpeaks(signal, r_peaks)\n    skip_len = 5 # Parameter to specify number of r_peaks in one signal\n    max_seq_len = 1280 # Parameter to specify maximum sequence length\n    \n    for r_curr in range(0,int(r_peaks.shape[0]-(skip_len-1)),skip_len):\n        if ((r_peaks[r_curr]-W_LEN_1_4)<0) or ((r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4)>=len(signal)):           # not enough signal to segment\n            continue\n        segmented_signal = np.array(signal[r_peaks[r_curr]-W_LEN_1_4:r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4])        # segmenting a heartbeat\n        segmented_signal = list(segmented_signal)\n        #print(segmented_signal.shape)\n        \n        if(len(segmented_signal) < 1280):\n            for m in range(int(1280-len(segmented_signal))): # Zero Padding\n                segmented_signal.append(0)\n        else:\n            segmented_signal = (segmented_signal[:int(max_seq_len)])\n            \n        segmented_signal = np.array(segmented_signal)\n        \n        if(segmented_signal.shape != (1280,1)):    \n            segmented_signal = np.reshape(segmented_signal,(1280,1))\n            \n        if (normalization):             # Z-score normalization\n            if abs(np.std(segmented_signal))<1e-6:          # flat line ECG, will cause zero division error\n                continue\n            segmented_signal = (segmented_signal - np.mean(segmented_signal)) / np.std(segmented_signal)            \n              \n        #if not np.isnan(segmented_signal).any():                    # checking for nan, this will never happen\n            segmented_signals.append(segmented_signal)\n\n    return segmented_signals,r_peaks           # returning the segmented signals and the refined r-peaks\n\n###### Function to Read Records\n\ndef read_rec(rec_path):\n\n    \"\"\" \n    Function to read record and return Segmented Signals\n\n    INPUTS:-\n    1) rec_path : Path of the Record\n\n    OUTPUTS:-\n    1) seg_sigs : Final Segmented Signals\n\n    \"\"\"\n    number_of_peaks = 5 # For extracting the required number of peaks                                    \n    full_rec = (wfdb.rdrecord(rec_path)).p_signal[:,0] # Entire Record - Taking Signal from Lead-1\n\n    f = PchipInterpolator(np.arange(int(full_rec.shape[0])),full_rec) # Fitting Interpolation Function\n    num_samples = int(full_rec.shape[0]) # Total Samples in 1000Hz Signal\n    num_samples_final = int(num_samples*(360/1000))\n    x_samp = (np.arange(num_samples)*(1000/360))[:num_samples_final] # Fixing Interpolation Input Values\n    full_rec_interp = f(x_samp)  # Intepolating Values \n    \n    r_peaks_init = ecg.hamilton_segmenter(full_rec_interp,360)[0] # R-Peak Segmentation and input is the signal frequency of 500Hz in this case\n    final_peak_index = r_peaks_init[int(r_peaks_init.shape[0] - int((r_peaks_init.shape[0]%number_of_peaks)))-1]\n    r_peaks_final = r_peaks_init[:final_peak_index] # Final Number of R_Peaks\n    full_rec_final = full_rec_interp[:int(r_peaks_final[-1]+W_LEN)] # Final Sequence\n    seg_sigs, r_peaks_ref = segmentSignals(full_rec_final,list(r_peaks_final)) # Final Signal Segmentation\n\n    return seg_sigs","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:24:21.968846Z","iopub.execute_input":"2021-08-02T11:24:21.969268Z","iopub.status.idle":"2021-08-02T11:24:21.992671Z","shell.execute_reply.started":"2021-08-02T11:24:21.969232Z","shell.execute_reply":"2021-08-02T11:24:21.991613Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Extracting List of the Elements with Two Sessions \ndir = '../input/ptb-dataset/ptb-diagnostic-ecg-database-1.0.0'\ntotal_index = 0\n#subjects_with_two = []\nsubjects = []\n\nfor item in np.sort(os.listdir(dir)):\n    #print('----------------------------------')\n    #print(item)\n    dir_sub = os.path.join(dir,item)\n    if(os.path.isdir(dir_sub)):\n        #ubjects.append(item)\n        #print(len(os.listdir(dir_sub))//3)\n        if(len(os.listdir(dir_sub))//3 >= 2):\n            subjects.append(item)\n            #total_index = total_index+1   \n            #print(item)\n        #    subjects_with_two.append(item)\n    #print('----------------------------------')\n\n#print(total_index)\n#print(subjects_with_two)\n\n#subjects = shuffle(subjects)[:100] # Shuffling and Taking 100 Subjects \n#print(subjects)\n\nprint(subjects)\nprint(len(subjects))","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:24:29.124734Z","iopub.execute_input":"2021-08-02T11:24:29.125118Z","iopub.status.idle":"2021-08-02T11:24:30.414124Z","shell.execute_reply.started":"2021-08-02T11:24:29.125084Z","shell.execute_reply":"2021-08-02T11:24:30.413129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Creation of Numpy Arrays \nmain_dir = '../input/ptb-dataset/ptb-diagnostic-ecg-database-1.0.0'\n\nX_train = []\nX_dev = []\ny_train = []\ny_dev = []\n\ncurrent_index = 0\n\n#for person_index,person_folder in enumerate(list(np.sort(os.listdir(main_dir)))):\n \nfor person_folder in np.sort(subjects):\n    \n    #if(current_index <= 231): # Taking 80% of the subjects for training \n    \n    person_folder_path = os.path.join(main_dir,person_folder)\n    person_folder_items = (list(np.sort(os.listdir(person_folder_path))))\n\n    for file_idx in range(0,len(person_folder_items),3):\n        file_path_list = str((os.path.join(person_folder_path,person_folder_items[file_idx])))\n        file_num = file_idx//3\n\n        rec_path = ''\n        for item_index in range(0,(len(file_path_list)-4)):\n            rec_path = rec_path+str(file_path_list[item_index])\n\n        seg_signal_current = read_rec(rec_path) # Extracting Records\n\n        # Division across Time\n        #for k in range(len(seg_signal_current)):\n\n        #    if(k <= np.round(len(seg_signal_current)*0.5)):\n        #        X_train.append(seg_signal_current[k])\n        #        y_train.append(current_index)\n\n        #    else:\n        #        X_dev.append(seg_signal_current[k])\n        #        y_dev.append(current_index)\n\n        if(file_num == 0):\n            for k in range(len(seg_signal_current)):\n                X_train.append(seg_signal_current[k])\n                y_train.append(current_index)\n                \n        if(file_num == 1):\n            for k in range(len(seg_signal_current)):\n                X_train.append(seg_signal_current[k])\n                y_train.append(current_index)\n\n    current_index = current_index+1\n    print('Processed for Person - '+str(current_index))\n\n##### Creation of Numpy Arrays\nX_train_CD_train_PTB = np.array(X_train)\ny_train_CD_train_PTB = np.array(y_train)\n\nprint(np.array(X_train).shape)\nprint(np.array(y_train).shape)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:24:37.960813Z","iopub.execute_input":"2021-08-02T11:24:37.961189Z","iopub.status.idle":"2021-08-02T11:26:05.841954Z","shell.execute_reply.started":"2021-08-02T11:24:37.961155Z","shell.execute_reply":"2021-08-02T11:26:05.840771Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## ECG-1D Dataset","metadata":{}},{"cell_type":"code","source":"####### Dataset Creation \n\n###### Constants\nFS = 360\nW_LEN = 256\nW_LEN_1_4 = 256 // 4\nW_LEN_3_4 = 3 * (256 // 4)\n\n###### Function to Read a Record\ndef read_rec(rec_path):\n\n    \"\"\" \n    Function to read record and return Segmented Signals\n\n    INPUTS:-\n    1) rec_path : Path of the Record\n\n    OUTPUTS:-\n    1) seg_sigs : Final Segmented Signals\n\n    \"\"\"\n    number_of_peaks = 2 # For extracting the required number of peaks                                    \n    full_rec = (wfdb.rdrecord(rec_path)).p_signal[:,1] # Entire Record\n\n    f = PchipInterpolator(np.arange(10000),full_rec) # Fitting Interpolation Function\n    x_samp = (np.arange(10000)*(500/360))[:7200] # Fixing Interpolation Input Values\n    full_rec_interp = f(x_samp)  # Intepolating Values \n    r_peaks_init = ecg.hamilton_segmenter(full_rec_interp,360)[0] # R-Peak Segmentation and input is the signal frequency of 500Hz in this case\n    final_peak_index = r_peaks_init[int(r_peaks_init.shape[0] - int((r_peaks_init.shape[0]%number_of_peaks)))-1]\n    r_peaks_final = r_peaks_init[:final_peak_index] # Final Number of R_Peaks\n    full_rec_final = full_rec_interp[:int(r_peaks_final[-1]+W_LEN)] # Final Sequence\n    seg_sigs, r_peaks_ref = segmentSignals(full_rec_final,list(r_peaks_final)) # Final Signal Segmentation\n\n    return seg_sigs # Returning the Ouput of the Signal Segmentation\n\n###### Function to Segment Signals\n\n##### Function\ndef segmentSignals(signal, r_peaks_annot, normalization=True, person_id= None, file_id=None):\n    \n    \"\"\"\n    Segments signals based on the detected R-Peak\n    Args:\n        signal (numpy array): input signal\n        r_peaks_annot (int []): r-peak locations.\n        normalization (bool, optional): apply z-normalization or not? . Defaults to True.\n        person_id ([type], optional): [description]. Defaults to None.\n        file_id ([type], optional): [description]. Defaults to None.\n    Returns:\n            [tuple(numpy array,numpy array)]: segmented signals and refined r-peaks\n    \"\"\"\n    def refine_rpeaks(signal, r_peaks):\n        \"\"\"\n        Refines the detected R-peaks. If the R-peak is slightly shifted, this assigns the \n        highest point R-peak.\n        Args:\n            signal (numpy array): input signal\n            r_peaks (int []): list of detected r-peaks\n        Returns:\n            [numpy array]: refined r-peaks\n        \"\"\"\n        r_peaks2 = np.array(r_peaks)            # make a copy\n        for i in range(len(r_peaks)):\n            r = r_peaks[i]          # current R-peak\n            small_segment = signal[max(0,r-100):min(len(signal),r+100)]         # consider the neighboring segment of R-peak\n            r_peaks2[i] = np.argmax(small_segment) - 100 + r_peaks[i]           # picking the highest point\n            r_peaks2[i] = min(r_peaks2[i],len(signal))                          # the detected R-peak shouldn't be outside the signal\n            r_peaks2[i] = max(r_peaks2[i],0)                                    # checking if it goes before zero    \n        return r_peaks2                     # returning the refined r-peak list\n    \n    segmented_signals = []                      # array containing the segmented beats\n    \n    r_peaks = np.array(r_peaks_annot)\n\n    r_peaks = refine_rpeaks(signal, r_peaks)\n    skip_len = 5 # Parameter to specify number of r_peaks in one signal\n    max_seq_len = 1280 # Parameter to specify maximum sequence length\n    \n    for r_curr in range(0,int(r_peaks.shape[0]-(skip_len-1)),skip_len):\n        if ((r_peaks[r_curr]-W_LEN_1_4)<0) or ((r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4)>=len(signal)):           # not enough signal to segment\n            continue\n        segmented_signal = np.array(signal[r_peaks[r_curr]-W_LEN_1_4:r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4])        # segmenting a heartbeat\n        segmented_signal = list(segmented_signal)\n        #print(segmented_signal.shape)\n        \n        if(len(segmented_signal) < 1280):\n            for m in range(int(1280-len(segmented_signal))): # Zero Padding\n                segmented_signal.append(0)\n        else:\n            segmented_signal = (segmented_signal[:int(max_seq_len)])\n            \n        segmented_signal = np.array(segmented_signal)\n        \n        if(segmented_signal.shape != (1280,1)):    \n            segmented_signal = np.reshape(segmented_signal,(1280,1))\n            \n        if (normalization):             # Z-score normalization\n            if abs(np.std(segmented_signal))<1e-6:          # flat line ECG, will cause zero division error\n                continue\n            segmented_signal = (segmented_signal - np.mean(segmented_signal)) / np.std(segmented_signal)            \n              \n        #if not np.isnan(segmented_signal).any():                    # checking for nan, this will never happen\n            segmented_signals.append(segmented_signal)\n\n    return segmented_signals,r_peaks           # returning the segmented signals and the refined r-peaks","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:26:15.697796Z","iopub.execute_input":"2021-08-02T11:26:15.698177Z","iopub.status.idle":"2021-08-02T11:26:15.71885Z","shell.execute_reply.started":"2021-08-02T11:26:15.698143Z","shell.execute_reply":"2021-08-02T11:26:15.717838Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Numpy Array Creation \npath_to_dir = '../input/ecg1d/ecg-id-database-1.0.0'\ntotal_folders = 90\ncurrent_index = 113\n\nX_train = []\nX_dev = []\ny_train = []\ny_dev = []\n\n#for item in subjects_with_two:\nfor i in range(2,92):\n\n    if(i != 75):\n\n        print(i-1)\n        folder_path = os.path.join(path_to_dir,np.sort(os.listdir(path_to_dir))[i]) # Path Selection\n        #items_in_folder = int(len(folder_path)//3)\n        #current_storage_path = './5_Beat_Ecg_ECG1D'+'/person'+str(current_index)\n\n        #for j in os.listdir(item):\n\n        for j in range(2):\n\n            rec_path = folder_path+'/'+'rec'+'_'+str(j+1) # Path to Record\n            seg_signal_current = read_rec(rec_path)\n\n            if(j == 0):\n                for k in range(len(seg_signal_current)):\n                    #file_name_current = current_storage_path+'/'+str(j)+'_/'+str(k)\n                    #np.savez_compressed(file_name_current,seg_signal_current[k])\n                    X_train.append(seg_signal_current[k])\n                    y_train.append(current_index)\n\n            if(j == 1):\n                for k in range(len(seg_signal_current)):\n                    #file_name_current = current_storage_path+'/'+str(j)+'_/'+str(k)\n                    #np.savez_compressed(file_name_current,seg_signal_current[k])\n                    X_train.append(seg_signal_current[k])\n                    y_train.append(current_index)\n\n        current_index = current_index+1\n\n###### Creation of Numpy Arrays\nX_train_CD_train_ECG1D = np.array(X_train)\ny_train_CD_train_ECG1D = np.array(y_train)\n\n###### Checking Shape of the Arrays\nprint(np.array(X_train).shape)\nprint(np.array(y_train).shape)\n\nprint(np.max(y_train))\nprint(np.min(y_train))","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:26:46.791386Z","iopub.execute_input":"2021-08-02T11:26:46.791743Z","iopub.status.idle":"2021-08-02T11:26:52.028944Z","shell.execute_reply.started":"2021-08-02T11:26:46.791711Z","shell.execute_reply":"2021-08-02T11:26:52.02801Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##### Plotting and Testing the Dataset\n\nfor i in range(100,110):\n    print(y_train[i])\n    plt.plot(np.arange(1280),X_train[i])\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:27:23.794631Z","iopub.execute_input":"2021-08-02T11:27:23.794993Z","iopub.status.idle":"2021-08-02T11:27:25.182268Z","shell.execute_reply.started":"2021-08-02T11:27:23.794959Z","shell.execute_reply":"2021-08-02T11:27:25.181158Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Dataset Creation and Loading ","metadata":{}},{"cell_type":"markdown","source":"### Training Data","metadata":{}},{"cell_type":"code","source":"###### Amalgamating Datasets \nX_train = np.array(list(X_train_CD_train_PTB)+list(X_train_CD_train_ECG1D))\ny_train = np.array(list(y_train_CD_train_PTB)+list(y_train_CD_train_ECG1D))\n\nX_train,y_train = shuffle(X_train,y_train)\n\nprint(X_train.shape)\nprint(y_train.shape)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:27:49.294847Z","iopub.execute_input":"2021-08-02T11:27:49.29539Z","iopub.status.idle":"2021-08-02T11:27:49.38057Z","shell.execute_reply.started":"2021-08-02T11:27:49.295344Z","shell.execute_reply":"2021-08-02T11:27:49.379453Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Saving Numpy Arrays\nnp.savez_compressed('./X_train_CD_PEM.npz',np.array(X_train))\nnp.savez_compressed('./y_train_CD_PEM.npz',np.array(y_train))","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:27:55.638632Z","iopub.execute_input":"2021-08-02T11:27:55.639006Z","iopub.status.idle":"2021-08-02T11:27:59.471888Z","shell.execute_reply.started":"2021-08-02T11:27:55.638971Z","shell.execute_reply":"2021-08-02T11:27:59.470846Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##### Loading Dataset - Training Dataset\nX_train = np.array(np.load('./X_train_CD_PEM.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\ny_train = np.array(np.load('./y_train_CD_PEM.npz',allow_pickle=True)['arr_0'],dtype=np.float16)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:28:03.926487Z","iopub.execute_input":"2021-08-02T11:28:03.926875Z","iopub.status.idle":"2021-08-02T11:28:04.503188Z","shell.execute_reply.started":"2021-08-02T11:28:03.926838Z","shell.execute_reply":"2021-08-02T11:28:04.502221Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##### Converting Labels to Categorical Format\ny_train_ohot = tf.keras.utils.to_categorical(y_train)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:28:07.686212Z","iopub.execute_input":"2021-08-02T11:28:07.686584Z","iopub.status.idle":"2021-08-02T11:28:07.692121Z","shell.execute_reply.started":"2021-08-02T11:28:07.686551Z","shell.execute_reply":"2021-08-02T11:28:07.691013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##### Plotting and Testing the Dataset\n\nfor i in range(1000,1010):\n    print(y_train[i])\n    plt.plot(np.arange(1280),X_train[i])\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-02T11:28:09.576821Z","iopub.execute_input":"2021-08-02T11:28:09.577213Z","iopub.status.idle":"2021-08-02T11:28:10.923438Z","shell.execute_reply.started":"2021-08-02T11:28:09.577178Z","shell.execute_reply":"2021-08-02T11:28:10.922475Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Testing Data","metadata":{}},{"cell_type":"code","source":"###### Saving Numpy Arrays\nnp.savez_compressed('./X_train_CD_PEM_test.npz',np.array(X_train))\nnp.savez_compressed('./y_train_CD_PEM_test.npz',np.array(y_train))\nnp.savez_compressed('./X_dev_CD_PEM_test.npz',np.array(X_dev))\nnp.savez_compressed('./y_dev_CD_PEM_test.npz',np.array(y_dev))","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:16:27.517860Z","iopub.execute_input":"2021-08-02T18:16:27.518229Z","iopub.status.idle":"2021-08-02T18:18:38.973233Z","shell.execute_reply.started":"2021-08-02T18:16:27.518197Z","shell.execute_reply":"2021-08-02T18:18:38.972076Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"##### Loading Dataset - Testing Dataset\nX_train = np.array(np.load('./X_train_CD_PEM_test.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\nX_dev = np.array(np.load('./X_dev_CD_PEM_test.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\ny_train = np.load('./y_train_CD_PEM_test.npz',allow_pickle=True)['arr_0']\ny_dev = np.load('./y_dev_CD_PEM_test.npz',allow_pickle=True)['arr_0']","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:18:43.679066Z","iopub.execute_input":"2021-08-02T18:18:43.679460Z","iopub.status.idle":"2021-08-02T18:19:41.957420Z","shell.execute_reply.started":"2021-08-02T18:18:43.679428Z","shell.execute_reply":"2021-08-02T18:19:41.956248Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"##### Converting Labels to Categorical Format\ny_train_ohot = tf.keras.utils.to_categorical(y_train)\ny_dev_ohot = tf.keras.utils.to_categorical(y_dev)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:19:41.959237Z","iopub.execute_input":"2021-08-02T18:19:41.959565Z","iopub.status.idle":"2021-08-02T18:19:41.966583Z","shell.execute_reply.started":"2021-08-02T18:19:41.959528Z","shell.execute_reply":"2021-08-02T18:19:41.965350Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"# Model Making","metadata":{}},{"cell_type":"markdown","source":"## Self-Calibrated Convolution","metadata":{}},{"cell_type":"code","source":"###### Model Development : Self-Calibrated \n\n##### Defining Self-Calibrated Block\n\nrate_regularizer = 1e-5\nclass self_cal_Conv1D(tf.keras.layers.Layer):\n\n    \"\"\" \n    This is inherited class from keras.layers and shall be instatition of self-calibrated convolutions\n    \"\"\"\n    \n    def __init__(self,num_filters,kernel_size,num_features):\n    \n        #### Defining Essentials\n        super().__init__()\n        self.num_filters = num_filters\n        self.kernel_size = kernel_size\n        self.num_features = num_features # Number of Channels in Input\n\n        #### Defining Layers\n        self.conv2 = tf.keras.layers.Conv1D(self.num_features/2,self.kernel_size,padding='same',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32',activation='relu')\n        self.conv3 = tf.keras.layers.Conv1D(self.num_features/2,self.kernel_size,padding='same',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32',activation='relu')\n        self.conv4 = tf.keras.layers.Conv1D(self.num_filters/2,self.kernel_size,padding='same',activation='relu',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32')\n        self.conv1 = tf.keras.layers.Conv1D(self.num_filters/2,self.kernel_size,padding='same',activation='relu',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32')\n        self.upsample = tf.keras.layers.Conv1DTranspose(filters=int(self.num_features/2),kernel_size=5,strides=5)\n        #self.attention_layer = tf.keras.layers.Attention()\n        #self.lstm = tf.keras.layers.LSTM(int(self.num_features/2),return_sequences=True)\n        #self.layernorm = tf.keras.layers.LayerNormalization()\n    \n    def get_config(self):\n\n        config = super().get_config().copy()\n        config.update({\n            'num_filters': self.num_filters,\n            'kernel_size': self.kernel_size,\n            'num_features': self.num_features\n        })\n        return config\n    \n    \n    def call(self,X):\n       \n        \"\"\"\n          INPUTS : 1) X - Input Tensor of shape (batch_size,sequence_length,num_features)\n          OUTPUTS : 1) X - Output Tensor of shape (batch_size,sequence_length,num_features)\n        \"\"\"\n        \n        #### Dimension Extraction\n        b_s = (X.shape)[0] \n        seq_len = (X.shape)[1]\n        num_features = (X.shape)[2]\n        \n        #### Channel-Wise Division\n        X_attention = X[:,:,0:int(self.num_features/2)]\n        X_global = X[:,:,int(self.num_features/2):]\n        \n        #### Self Calibration Block\n\n        ### Local Feature Detection\n\n        ## Down-Sampling\n        #x1 = X_attention[:,0:int(seq_len/5),:]\n        #x2 = X_attention[:,int(seq_len/5):int(seq_len*(2/5)),:]\n        #x3 = X_attention[:,int(seq_len*(2/5)):int(seq_len*(3/5)),:]\n        #x4 = X_attention[:,int(seq_len*(3/5)):int(seq_len*(4/5)),:]\n        #x5 = X_attention[:,int(seq_len*(4/5)):seq_len,:]\n        x_down_sampled = tf.keras.layers.AveragePooling1D(pool_size=5,strides=5)(X_attention)\n        \n        ## Convoluting Down Sampled Sequence \n        #x1 = self.conv2(x1)\n        #x2 = self.conv2(x2)\n        #x3 = self.conv2(x3)\n        #x4 = self.conv2(x4)\n        #x5 = self.conv2(x5)\n        x_down_conv = self.conv2(x_down_sampled)\n        #x_down_feature = self.attention_layer([x_down_sampled,x_down_sampled])\n        #x_down_feature = self.lstm(x_down_sampled)\n        #x_down_feature = self.layernorm(x_down_feature)\n        \n        ## Up-Sampling\n        x_down_upsampled = self.upsample(x_down_conv)   \n        #X_local_upsampled = tf.keras.layers.concatenate([x1,x2,x3,x4,x5],axis=1)\n\n        ## Local-CAM\n        X_local = X_attention + x_down_upsampled  #X_local_upsampled\n\n        ## Local Importance \n        X_2 = tf.keras.activations.sigmoid(X_local)\n\n        ### Self-Calibration\n\n        ## Global Convolution\n        X_3 = self.conv3(X_attention)\n\n        ## Attention Determination\n        X_attention = tf.math.multiply(X_2,X_3)\n\n        #### Self-Calibration Feature Extraction\n        X_4 = self.conv4(X_attention)\n\n        #### Normal Feature Extraction\n        X_1 = self.conv1(X_global)\n\n        #### Concatenating and Returning Output\n        return (tf.keras.layers.concatenate([X_1,X_4],axis=2))","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:19:41.969120Z","iopub.execute_input":"2021-08-02T18:19:41.969623Z","iopub.status.idle":"2021-08-02T18:19:41.995255Z","shell.execute_reply.started":"2021-08-02T18:19:41.969562Z","shell.execute_reply":"2021-08-02T18:19:41.993990Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"## Transformer","metadata":{}},{"cell_type":"code","source":"def get_angles(pos, i, d_model):\n    angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n    return pos * angle_rates\n\ndef positional_encoding(position, d_model):\n    angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n                          np.arange(d_model)[np.newaxis, :],\n                          d_model)\n  \n  # apply sin to even indices in the array; 2i\n    angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n  \n  # apply cos to odd indices in the array; 2i+1\n    angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n    \n    pos_encoding = angle_rads[np.newaxis, ...]\n    \n    return tf.cast(pos_encoding, dtype=tf.float32)\n\ndef create_padding_mask(seq):\n    seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n  \n    # add extra dimensions to add the padding\n    # to the attention logits. \n    return seq[:, tf.newaxis, tf.newaxis, :]  # (batch_size, 1, 1, seq_len)\n\ndef scaled_dot_product_attention(q, k, v, mask):\n    \"\"\"Calculate the attention weights.\n    q, k, v must have matching leading dimensions.\n    k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.\n    The mask has different shapes depending on its type(padding or look ahead) \n    but it must be broadcastable for addition.\n\n    Args:\n    q: query shape == (..., seq_len_q, depth)\n    k: key shape == (..., seq_len_k, depth)\n    v: value shape == (..., seq_len_v, depth_v)\n    mask: Float tensor with shape broadcastable \n          to (..., seq_len_q, seq_len_k). Defaults to None.\n\n    Returns:\n    output, attention_weights\n    \"\"\"\n\n    matmul_qk = tf.matmul(q, k, transpose_b=True)  # (..., seq_len_q, seq_len_k)\n  \n    # scale matmul_qk\n    dk = tf.cast(tf.shape(k)[-1], tf.float32)\n    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n\n    # add the mask to the scaled tensor.\n    if mask is not None:\n        scaled_attention_logits += (mask * -1e9)  \n\n    # softmax is normalized on the last axis (seq_len_k) so that the scores\n    # add up to 1.\n    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)  # (..., seq_len_q, seq_len_k)\n\n    output = tf.matmul(attention_weights, v)  # (..., seq_len_q, depth_v)\n\n    return output, attention_weights\n\nclass MultiHeadAttention(tf.keras.layers.Layer):\n    \n    def __init__(self, d_model, num_heads):\n        super(MultiHeadAttention, self).__init__()\n        self.num_heads = num_heads\n        self.d_model = d_model\n\n        assert d_model % self.num_heads == 0\n\n        self.depth = d_model // self.num_heads\n\n        self.wq = tf.keras.layers.Dense(d_model)\n        self.wk = tf.keras.layers.Dense(d_model)\n        self.wv = tf.keras.layers.Dense(d_model)\n\n        self.dense = tf.keras.layers.Dense(d_model)\n\n    def get_config(self):\n        config = super(MultiHeadAttention, self).get_config().copy()\n        config.update({\n            'd_model': self.d_model,\n            'num_heads':self.num_heads\n        })\n        \n    def split_heads(self, x, batch_size):\n        \n        \"\"\"Split the last dimension into (num_heads, depth).\n        Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)\n        \"\"\"\n        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n        return tf.transpose(x, perm=[0, 2, 1, 3])\n    \n    def call(self, v, k, q, mask):\n        batch_size = tf.shape(q)[0]\n\n        q = self.wq(q)  # (batch_size, seq_len, d_model)\n        k = self.wk(k)  # (batch_size, seq_len, d_model)\n        v = self.wv(v)  # (batch_size, seq_len, d_model)\n\n        q = self.split_heads(q, batch_size)  # (batch_size, num_heads, seq_len_q, depth)\n        k = self.split_heads(k, batch_size)  # (batch_size, num_heads, seq_len_k, depth)\n        v = self.split_heads(v, batch_size)  # (batch_size, num_heads, seq_len_v, depth)\n\n        # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\n        # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\n        scaled_attention, attention_weights = scaled_dot_product_attention(\n            q, k, v, mask)\n\n        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])  # (batch_size, seq_len_q, num_heads, depth)\n\n        concat_attention = tf.reshape(scaled_attention, \n                                      (batch_size, -1, self.d_model))  # (batch_size, seq_len_q, d_model)\n\n        output = self.dense(concat_attention)  # (batch_size, seq_len_q, d_model)\n\n        return output, attention_weights\n\ndef point_wise_feed_forward_network(d_model, dff):\n    return tf.keras.Sequential([\n      tf.keras.layers.Dense(dff, activation='relu'),  # (batch_size, seq_len, dff)\n      tf.keras.layers.Dense(d_model)  # (batch_size, seq_len, d_model)\n  ])\n\nclass Encoder(tf.keras.layers.Layer):\n    def __init__(self, num_layers, d_model, num_heads, dff,\n               maximum_position_encoding, rate=0.1):\n        super(Encoder, self).__init__()\n\n        self.d_model = d_model\n        self.num_layers = num_layers\n        self.num_heads = num_heads\n        self.dff = dff\n        self.maximum_position_encoding = maximum_position_encoding\n        self.rate = rate\n\n        #self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)\n        self.pos_encoding = positional_encoding(maximum_position_encoding, \n                                                self.d_model)\n\n\n        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) \n                           for _ in range(num_layers)]\n\n        self.dropout = tf.keras.layers.Dropout(rate)\n        \n    def get_config(self):\n        config = super(Encoder, self).get_config().copy()\n        config.update({\n            'num_layers': self.num_layers,\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'maximum_position_encoding':self.maximum_position_encoding,\n            'rate':self.rate  \n        })\n        \n    def call(self, x, training, mask):\n\n        seq_len = tf.shape(x)[1]\n\n        # adding embedding and position encoding.\n        #x = self.embedding(x)  # (batch_size, input_seq_len, d_model)\n        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n        x += self.pos_encoding[:, :seq_len, :]\n\n        x = self.dropout(x, training=training)         \n\n        for i in range(self.num_layers):\n            x = self.enc_layers[i](x, training, mask)\n\n        return x  # (batch_size, input_seq_len, d_model)\n\nclass EncoderLayer(tf.keras.layers.Layer):\n    def __init__(self, d_model, num_heads, dff, rate=0.1):\n        super(EncoderLayer, self).__init__()\n        \n        self.d_model = d_model\n        self.num_heads = num_heads\n        self.dff = dff\n        self.rate = rate\n\n        self.mha = MultiHeadAttention(d_model, num_heads)\n        self.ffn = point_wise_feed_forward_network(d_model, dff)\n\n        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n\n        self.dropout1 = tf.keras.layers.Dropout(rate)\n        self.dropout2 = tf.keras.layers.Dropout(rate)\n        \n    def get_config(self):\n        config = super(EncoderLayer, self).get_config().copy()\n        config.update({\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'rate':self.rate  \n        })\n        \n    def call(self, x, training, mask):\n\n        attn_output, _ = self.mha(x, x, x, mask)  # (batch_size, input_seq_len, d_model)\n        attn_output = self.dropout1(attn_output, training=training)\n        out1 = self.layernorm1(x + attn_output)  # (batch_size, input_seq_len, d_model)\n\n        ffn_output = self.ffn(out1)  # (batch_size, input_seq_len, d_model)\n        ffn_output = self.dropout2(ffn_output, training=training)\n        out2 = self.layernorm2(out1 + ffn_output)  # (batch_size, input_seq_len, d_model)\n    \n        return out2\n    \nclass Transformer(tf.keras.Model):\n    def __init__(self, num_layers, d_model, num_heads, dff, \n                 pe_input, rate=0.1):\n        super(Transformer, self).__init__()\n        \n        self.num_layers = num_layers\n        self.d_model = d_model\n        self.num_heads = num_heads\n        self.dff = dff\n        self.pe_input = pe_input\n        self.rate = rate\n        \n        self.encoder = Encoder(num_layers, d_model, num_heads, dff, \n                                pe_input, rate)\n        \n    def get_config(self):\n        config = super(Transformer,self).get_config().copy()\n        config.update({\n            'num_layers': self.num_layers,\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'pe_input':self.pe_input,\n            'rate':self.rate  \n        })\n    \n    def call(self, inp, training, enc_padding_mask):\n        return self.encoder(inp, training, enc_padding_mask)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:19:41.998442Z","iopub.execute_input":"2021-08-02T18:19:41.999025Z","iopub.status.idle":"2021-08-02T18:19:42.048464Z","shell.execute_reply.started":"2021-08-02T18:19:41.998977Z","shell.execute_reply":"2021-08-02T18:19:42.046956Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"## ArcFace Loss","metadata":{}},{"cell_type":"code","source":"class ArcFace(tf.keras.layers.Layer):\n    \n    def __init__(self, n_classes, s, m,regularizer):\n        super().__init__()\n        self.n_classes = n_classes\n        self.s = s\n        self.m = m\n        self.regularizer = tf.keras.regularizers.get(regularizer)\n\n    def get_config(self):\n\n        config = super().get_config().copy()\n        config.update({\n            'n_classes': self.n_classes,\n            's': self.s,\n            'm': self.m,\n            'regularizer': self.regularizer\n        })\n        return config\n\n    def build(self, input_shape):\n        super(ArcFace, self).build(input_shape[0])\n        self.W = self.add_weight(name='W',\n                                shape=(input_shape[0][-1], self.n_classes),\n                                initializer='glorot_uniform',\n                                trainable=True\n                                )\n\n    def call(self, inputs):\n        x, y = inputs\n        c = tf.keras.backend.shape(x)[-1]\n        # normalize feature\n        x = tf.nn.l2_normalize(x, axis=1)\n        # normalize weights\n        W = tf.nn.l2_normalize(self.W, axis=0)\n        # dot product\n        logits = x @ W\n        # add margin\n        # clip logits to prevent zero division when backward\n        theta = tf.acos(tf.keras.backend.clip(logits, -1.0 + tf.keras.backend.epsilon(), 1.0 - tf.keras.backend.epsilon()))\n        target_logits = tf.cos(theta + self.m)\n        # sin = tf.sqrt(1 - logits**2)\n        # cos_m = tf.cos(logits)\n        # sin_m = tf.sin(logits)\n        # target_logits = logits * cos_m - sin * sin_m\n        #\n        logits = logits * (1 - y) + target_logits * y\n        # feature re-scale\n        logits *= self.s\n        out = tf.nn.softmax(logits)    \n        return out\n\n    def compute_output_shape(self, input_shape):\n        return (None, self.n_classes)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:19:42.050084Z","iopub.execute_input":"2021-08-02T18:19:42.050421Z","iopub.status.idle":"2021-08-02T18:19:42.071419Z","shell.execute_reply.started":"2021-08-02T18:19:42.050389Z","shell.execute_reply":"2021-08-02T18:19:42.069821Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"# Model Training","metadata":{}},{"cell_type":"code","source":"####### Phase-1 Models\n###### Defining Architecture\n\nwith tpu_strategy.scope():\n\n    ##### SC_Module \n\n    #### Defining Hyperparameters\n    num_layers = 2\n    d_model = 512\n    num_heads = 8\n    dff = 1024\n    max_seq_len = 1280 #X_train.shape[1]\n    pe_input = 320\n    rate = 0.5\n    num_features = 1\n    num_classes = 47\n\n    #### Defining Layers\n    Input_layer = tf.keras.layers.Input(shape=(max_seq_len,num_features))\n    self_conv1 = self_cal_Conv1D(128,15,128)\n    self_conv2 = self_cal_Conv1D(128,20,128) # Newly Added\n    self_conv3 = self_cal_Conv1D(256,15,128)\n    self_conv4 = self_cal_Conv1D(256,20,256) # Newly Added\n    self_conv5 = self_cal_Conv1D(512,15,256)\n    self_conv6 = self_cal_Conv1D(512,20,512) # Newly Added\n    self_conv7 = self_cal_Conv1D(1024,15,512)\n    self_conv8 = self_cal_Conv1D(1024,20,1024) # Newly Added\n    conv_initial = tf.keras.layers.Conv1D(32,15,padding='same',activation='relu')\n    conv_second = tf.keras.layers.Conv1D(64,15,padding='same',activation='relu')\n    conv_third = tf.keras.layers.Conv1D(128,15,padding='same',activation='relu')\n    #lstm1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128,activation='tanh',return_sequences=True),merge_mode='ave')\n    transform_1 = tf.keras.layers.Conv1D(128,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_2 = tf.keras.layers.Conv1D(256,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_3 = tf.keras.layers.Conv1D(512,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_4 = tf.keras.layers.Conv1D(1024,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transformer = Transformer(num_layers,d_model,num_heads,dff,pe_input,rate)\n    gap_layer = tf.keras.layers.GlobalAveragePooling1D()\n    arc_logit_layer = ArcFace(47,30.0,0.3,tf.keras.regularizers.l2(1e-4))\n\n    #### Defining Architecture\n    ### Input Layer\n    Inputs = Input_layer\n    Input_Labels = tf.keras.layers.Input(shape=(num_classes,))\n\n    ### Initial Convolutional Layers\n    conv_initial = conv_initial(Inputs)\n    #conv_initial = tf.keras.layers.LayerNormalization()(conv_initial)\n    #conv_initial = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_initial)     \n    #conv_initial = tf.keras.layers.Add()([conv_initial,Inputs])\n    \n    conv_second = conv_second(conv_initial)\n    #conv_second = tf.keras.layers.LayerNormalization()(conv_second)\n    #conv_second = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_second)\n    #conv_second = tf.keras.layers.Add()([conv_second,conv_initial])\n    #conv_second = tf.keras.layers.concatenate(axis=2)([conv_initial,conv_second])\n    \n    conv_third = conv_third(conv_second)\n    #conv_third = tf.keras.layers.LayerNormalization()(conv_third)\n    #conv_third = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_third)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(Inputs)\n    #conv_third = tf.keras.layers.Add()([conv_third,conv_second])\n    #conv_third = tf.keras.layers.concatenate(axis=2)([conv_initial,conv_second,conv_third])\n    #conv_third = lstm1(conv_second)\n    #conv_third = tf.keras.layers.Attention()([conv_third,conv_third])\n    \n    ### 1st Residual Block\n    transform_1 = transform_1(conv_third)\n    conv1 = self_conv1(conv_third)\n    #conv1 = tf.keras.layers.AlphaDropout(rate=0.2)(conv1)\n    conv2 = self_conv2(conv1)\n    #conv2 = tf.keras.layers.AlphaDropout(rate=0.2)(conv2)\n    conv2 = tf.keras.layers.Add()([conv2,transform_1])\n    #conv2 = tf.keras.layers.LayerNormalization()(conv2)\n    conv2 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv2)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(mask)    \n\n    ### 2nd Residual Block\n    #conv_third = tf.keras.layers.Attention()([conv_third,conv_third])\n    transform_2 = transform_2(conv2)\n    conv3 = self_conv3(conv2)\n    #conv3 = tf.keras.layers.AlphaDropout(rate=0.2)(conv3)\n    conv4 = self_conv4(conv3)\n    #conv4 = tf.keras.layers.AlphaDropout(rate=0.2)(conv4)\n    conv4 = tf.keras.layers.Add()([conv4,transform_2])\n    #conv4 = tf.keras.layers.LayerNormalization()(conv4)\n    conv4 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv4)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(mask)\n\n    ### 3rd Residual Block\n    transform_3 = transform_3(conv4)\n    conv5 = self_conv5(conv4)\n    #conv5 = tf.keras.layers.AlphaDropout(rate=0.2)(conv5)\n    conv6 = self_conv6(conv5)\n    #conv6 = tf.keras.layers.AlphaDropout(rate=0.2)(conv6)\n    conv6 = tf.keras.layers.Add()([conv6,transform_3])\n    #conv6 = tf.keras.layers.LayerNormalization()(conv6)\n    #conv6 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv6)\n\n    ### 4th Residual Block\n    #transform_4 = transform_4(conv6)\n    #conv7 = self_conv7(conv6)\n    #conv8 = self_conv8(conv7)\n    #conv8 = tf.keras.layers.Add()([conv8,transform_4])\n\n    ### Transformer\n    ## Wide-Head Attention Model\n    #tx_embedding = tf.keras.layers.Lambda(PE_Layer)(Inputs)\n    #tx_embedding = tf.keras.layers.Dropout(rate)(tx_embedding,training=True)\n    #mask_reshaped = tf.keras.layers.Reshape((max_seq_len,))(Inputs)\n    #encoder_op1 = encoder_block1(tx_embedding,mask_reshaped)\n    #encoder_op2 = encoder_block2(encoder_op1,mask_reshaped)\n\n    ## Narrow-Head Attention Model\n    #mask_reshaped = tf.keras.layers.Reshape((160,))(mask)\n    embeddings =  transformer(inp=conv6,enc_padding_mask=None)\n    #embeddings = transformer(inp=conv6,enc_padding_mask=create_padding_mask(mask))\n    #residual_embeddings = tf.keras.layers.Add()([conv6,embeddings])\n\n    ### Output Layers\n    ## Initial Layers\n    gap_op = gap_layer(embeddings)\n    dense1 = tf.keras.layers.Dense(256,activation='relu')(gap_op)\n    dropout1 = tf.keras.layers.Dropout(rate)(dense1)\n    \n    ## ArcFace Output Network\n    dense2 = tf.keras.layers.Dense(256,kernel_initializer='he_normal',\n                kernel_regularizer=tf.keras.regularizers.l2(1e-4))(dropout1)\n    ##dense2 = tf.keras.layers.BatchNormalization()(dense2)\n    dense3 = arc_logit_layer(([dense2,Input_Labels]))\n    \n    ## Softmax Output Network\n    #dense2 = tf.keras.layers.Dense(256,activation='relu')(dropout1)\n    ###dropout2 = tf.keras.layers.Dropout(rate)(dense2) # Not to be included\n    #dense3 = tf.keras.layers.Dense(35,activation='softmax')(dense2)\n\n    #### Compiling Architecture            \n    ### ArcFace Model Compilation\n    model = tf.keras.models.Model(inputs=[Inputs,Input_Labels],outputs=dense3)\n    model.load_weights('../input/cd-pem/CD_PEM.h5')\n    model.compile(tf.keras.optimizers.Adam(lr=1e-4,clipnorm=1.0),loss='categorical_crossentropy',metrics=['accuracy'])\n    ### Softmax Model Compilation\n    #model = tf.keras.models.Model(inputs=Inputs,outputs=dense3)\n\nmodel.summary()      \ntf.keras.utils.plot_model(model)\n##### Model Training \n\n#### Model Checkpointing\n#! mkdir './Models'\n#filepath= \"/content/Models/saved-model-{epoch:02d}-{val_accuracy:.2f}.h5\"\n#checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath,monitor='val_accuracy',save_best_only=False,mode='max')\nfilepath = './CD_PEM.h5'\ncheckpoint = tf.keras.callbacks.ModelCheckpoint(filepath,monitor='val_accuracy',save_best_only=True,mode='max',save_weights_only=True)\n\n#### Custom Learning Rate Schedule\n#def build_lrfn(lr_start=1e-4, lr_max=1e-3, \n#               lr_min=1e-6, lr_rampup_epochs=5, \n#               lr_sustain_epochs=0, lr_exp_decay=.87):\n#    lr_max = lr_max * tpu_strategy.num_replicas_in_sync\n\n#    def lrfn(epoch):\n#        if epoch < lr_rampup_epochs:\n#            lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start\n#        elif epoch < lr_rampup_epochs + lr_sustain_epochs:\n#            lr = lr_max\n#        else:\n#            lr = (lr_max - lr_min) * lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min#\n#        return lr\n#    \n#    return lrfn\n\n#lrfn = build_lrfn()\n#lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=1)\n#callback_list = [checkpoint,  lr_callback]\n\n#### Model Training\n#### Model Training\n### ArcFace Training\n#history = model.fit((X_train,y_train_ohot),y_train_ohot,epochs=500,batch_size=128,\n#                validation_split=0.1,validation_batch_size=128,\n#               callbacks=checkpoint)\n\n### Softmax Training \n#history = model.fit(X_train,y_train_ohot,epochs=250,batch_size=128,\n#                validation_data=(X_dev,y_dev_ohot),validation_batch_size=128,\n#                callbacks=checkpoint)\n\n\n##### Plotting Metrics  \n#### Accuracy and Loss Plots \n\n### Accuracy\n#plt.plot(history.history['accuracy'])\n#plt.plot(history.history['val_accuracy'])\n#plt.title('Model Accuracy')\n#plt.ylabel('Accuracy')\n#plt.xlabel('Epoch')  \n#plt.legend(['Train', 'Validation'], loc='best')\n#plt.show()\n\n### Loss     \n#plt.plot(history.history['loss'])  \n#plt.plot(history.history['val_loss'])\n#plt.title('Model Loss')  \n#plt.ylabel('Loss')         \n#plt.xlabel('epoch')\n#plt.legend(['Train', 'Validation'], loc='best')   \n#plt.show()      \n\n##### Saving Model            \n#model.save_weights('ECG_SCNRNet.h5' )","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:21:44.029599Z","iopub.execute_input":"2021-08-02T18:21:44.030113Z","iopub.status.idle":"2021-08-02T18:21:52.474665Z","shell.execute_reply.started":"2021-08-02T18:21:44.030074Z","shell.execute_reply":"2021-08-02T18:21:52.473152Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Model: \"model\"\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_1 (InputLayer)            [(None, 1280, 1)]    0                                            \n__________________________________________________________________________________________________\nconv1d_32 (Conv1D)              (None, 1280, 32)     512         input_1[0][0]                    \n__________________________________________________________________________________________________\nconv1d_33 (Conv1D)              (None, 1280, 64)     30784       conv1d_32[0][0]                  \n__________________________________________________________________________________________________\nconv1d_34 (Conv1D)              (None, 1280, 128)    123008      conv1d_33[0][0]                  \n__________________________________________________________________________________________________\nself_cal__conv1d (self_cal_Conv (None, 1280, 128)    266560      conv1d_34[0][0]                  \n__________________________________________________________________________________________________\nself_cal__conv1d_1 (self_cal_Co (None, 1280, 128)    348480      self_cal__conv1d[0][0]           \n__________________________________________________________________________________________________\nconv1d_35 (Conv1D)              (None, 1280, 128)    49280       conv1d_34[0][0]                  \n__________________________________________________________________________________________________\nadd (Add)                       (None, 1280, 128)    0           self_cal__conv1d_1[0][0]         \n                                                                 conv1d_35[0][0]                  \n__________________________________________________________________________________________________\nmax_pooling1d (MaxPooling1D)    (None, 640, 128)     0           add[0][0]                        \n__________________________________________________________________________________________________\nself_cal__conv1d_2 (self_cal_Co (None, 640, 256)     389568      max_pooling1d[0][0]              \n__________________________________________________________________________________________________\nself_cal__conv1d_3 (self_cal_Co (None, 640, 256)     1393280     self_cal__conv1d_2[0][0]         \n__________________________________________________________________________________________________\nconv1d_36 (Conv1D)              (None, 640, 256)     98560       max_pooling1d[0][0]              \n__________________________________________________________________________________________________\nadd_1 (Add)                     (None, 640, 256)     0           self_cal__conv1d_3[0][0]         \n                                                                 conv1d_36[0][0]                  \n__________________________________________________________________________________________________\nmax_pooling1d_1 (MaxPooling1D)  (None, 320, 256)     0           add_1[0][0]                      \n__________________________________________________________________________________________________\nself_cal__conv1d_4 (self_cal_Co (None, 320, 512)     1557376     max_pooling1d_1[0][0]            \n__________________________________________________________________________________________________\nself_cal__conv1d_5 (self_cal_Co (None, 320, 512)     5571840     self_cal__conv1d_4[0][0]         \n__________________________________________________________________________________________________\nconv1d_37 (Conv1D)              (None, 320, 512)     393728      max_pooling1d_1[0][0]            \n__________________________________________________________________________________________________\nadd_2 (Add)                     (None, 320, 512)     0           self_cal__conv1d_5[0][0]         \n                                                                 conv1d_37[0][0]                  \n__________________________________________________________________________________________________\ntransformer (Transformer)       (None, 320, 512)     4205568     add_2[0][0]                      \n__________________________________________________________________________________________________\nglobal_average_pooling1d (Globa (None, 512)          0           transformer[0][0]                \n__________________________________________________________________________________________________\ndense_12 (Dense)                (None, 256)          131328      global_average_pooling1d[0][0]   \n__________________________________________________________________________________________________\ndropout_5 (Dropout)             (None, 256)          0           dense_12[0][0]                   \n__________________________________________________________________________________________________\ndense_13 (Dense)                (None, 256)          65792       dropout_5[0][0]                  \n__________________________________________________________________________________________________\ninput_2 (InputLayer)            [(None, 47)]         0                                            \n__________________________________________________________________________________________________\narc_face (ArcFace)              (None, 47)           12032       dense_13[0][0]                   \n                                                                 input_2[0][0]                    \n==================================================================================================\nTotal params: 14,637,696\nTrainable params: 14,637,696\nNon-trainable params: 0\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Model Testing","metadata":{}},{"cell_type":"markdown","source":"## KNN based ArcFace Loss Testing","metadata":{}},{"cell_type":"code","source":"###### Testing Model - ArcFace Style                \nwith tpu_strategy.scope():     \n\n    def normalisation_layer(x):   \n        return(tf.math.l2_normalize(x, axis=1, epsilon=1e-12))\n\n    #X_dev_flipped = tf.image.flip_up_down(X_dev)  \n    #x_train_flipped = tf.image.flip_up_down(X_train_final)\n\n    predictive_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-3].output)\n    predictive_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\nwith tpu_strategy.scope():\n    y_in = tf.keras.layers.Input((47,))\n\n    Input_Layer = tf.keras.layers.Input((1280,1))\n    op_1 = predictive_model([Input_Layer,y_in])\n\n    ##Input_Layer_Flipped = tf.keras.layers.Input((224,224,3))\n    ##op_2 = predictive_model([Input_Layer_Flipped,y_in]) \n    ##final_op = tf.keras.layers.Concatenate(axis=1)(op_1)\n\n    final_norm_op = tf.keras.layers.Lambda(normalisation_layer)(op_1)\n\n    testing_model = tf.keras.models.Model(inputs=[Input_Layer,y_in],outputs=final_norm_op)\n    testing_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\n##### Nearest Neighbor Classification\nfrom sklearn.neighbors import KNeighborsClassifier\n#Test_Embeddings = testing_model.predict((X_dev,y_dev_ohot))\n#Train_Embeddings = testing_model.predict((X_train,y_train_ohot))\n\n#col_mean = np.nanmean(Test_Embeddings, axis=0)\n#inds = np.where(np.isnan(Test_Embeddings))\n#print(inds)\n#Test_Embeddings[inds] = np.take(col_mean, inds[1])\n\n#col_mean = np.nanmean(Train_Embeddings, axis=0)                                                                                                                                \n#inds = np.where(np.isnan(Train_Embeddings))\n#print(inds)\n#Train_Embeddings[inds] = np.take(col_mean, inds[1])\n\n#Test_Embeddings = np.nan_to_num(Test_Embeddings)\n\n##### Refining Test Embeddings\n#for i in range(Train_Embeddings.shape[0]):\n#    for j in range(Train_Embeddings.shape[1]):\n#        if(math.isnan(Train_Embeddings[i,j])):\n#            Train_Embeddings[i,j] == 0\n#        if(Train_Embeddings[i,j]>1e4):\n#            Train_Embeddings[i,j] == 1e4\n\n##### Refining Train Embeddings    \n#for i in range(Test_Embeddings.shape[0]):\n#    for j in range(Test_Embeddings.shape[1]):\n#        if(math.isnan(Test_Embeddings[i,j])):\n#            Test_Embeddings[i,j] == 0\n#        if(Test_Embeddings[i,j]>1e4 or math.isinf(Test_Embeddings[i,j])):\n#            Test_Embeddings[i,j] == 1e4\n\n#del(X_train_final,X_dev,X_dev_flipped,x_train_flipped)\n#gc.collect()\n\nTest_Accuracy_With_Train = []\nTest_Accuracy_With_Test = []\n                                                                     \nfor k in range(1,5):\n    knn = KNeighborsClassifier(n_neighbors=k,metric='euclidean')\n    knn.fit(Train_Embeddings,y_train)\n    Test_Accuracy_With_Train.append(knn.score(Test_Embeddings,y_dev))\n    knn.fit(Test_Embeddings,y_dev)\n    Test_Accuracy_With_Test.append(knn.score(Test_Embeddings,y_dev))\n\nprint('--------------------------------')\nprint(np.max(Test_Accuracy_With_Train))\nprint(np.max(Test_Accuracy_With_Test))\nprint('--------------------------------')\nprint(np.mean(Test_Accuracy_With_Train))\nprint(np.mean(Test_Accuracy_With_Test))\nprint('--------------------------------')\nprint((Test_Accuracy_With_Train)[0])\nprint((Test_Accuracy_With_Test)[0])\nprint('--------------------------------')\n\nplt.plot(np.arange(1,5),np.array(Test_Accuracy_With_Train),label='Test_Accuracy_With_Train')\nplt.plot(np.arange(1,5),np.array(Test_Accuracy_With_Test),label='Test_Accuracy_With_Test')\nplt.title('Testing Accuracy vs Number of Neighbors')\nplt.xlabel('Number of Neighbors')\nplt.ylabel('Test Accuracy')\nplt.legend()       \nplt.show()                 \n\nnp.savez_compressed('Test_Embeddings_Baseline.npz',Test_Embeddings)\nnp.savez_compressed('Train_Embeddings_Baseline.npz',Train_Embeddings)","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:45:30.107744Z","iopub.execute_input":"2021-08-02T18:45:30.108192Z","iopub.status.idle":"2021-08-02T18:47:27.781297Z","shell.execute_reply.started":"2021-08-02T18:45:30.108157Z","shell.execute_reply":"2021-08-02T18:47:27.780102Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"--------------------------------\n0.9584702718852703\n1.0\n--------------------------------\n0.9537645652823424\n0.9862936958470272\n--------------------------------\n0.9584702718852703\n1.0\n--------------------------------\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## t-SNE","metadata":{}},{"cell_type":"code","source":"####### t-SNE Plot Generation\n###### Model Creation\n#with tpu_strategy.scope():          \n#    tsne_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-4].output)\n#    tsne_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n#tsne_model.summary()\n\n###### Model Predicted\n#embeddings_final = tsne_model.predict((X_dev,y_exp))\n\n###### t-SNE plot plotting\n##### Reduction to Lower Dimensions\ntsne_X_dev = TSNE(n_components=2,perplexity=30,learning_rate=10,n_iter=2000,n_iter_without_progress=50).fit_transform(Test_Embeddings)\n\n##### Plotting\nj = 0 # Index for rotating legend\nplt.rcParams[\"figure.figsize\"] = [24,16]\nmStyles = [\".\",\",\",\"o\",\"v\",\"^\",\"<\",\">\",\"1\",\"2\",\"3\",\"4\",\"8\",\"s\",\"p\",\"P\",\"*\",\"h\",\"H\",\"+\",\"x\",\"X\",\"D\",\"d\",\"|\",\"_\",\n           0,1,2,3,4,5,6,7,8,9,10,11,0,1,2,3,4,5,6,7,8,9,10]\nfor idx,color_index,marker_type in zip(list(np.arange(100)),sns.color_palette('muted',100),mStyles):\n    plt.scatter(tsne_X_dev[y_dev == idx, 0], tsne_X_dev[y_dev == idx, 1],marker=marker_type)\nplt.legend([str(j) for j in range(100)])\nplt.savefig('tsne_plot_5000_iters.png')\nplt.savefig('tsne_plot_5000_iters.pdf')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-02T18:47:27.782932Z","iopub.execute_input":"2021-08-02T18:47:27.783253Z","iopub.status.idle":"2021-08-02T18:49:03.247359Z","shell.execute_reply.started":"2021-08-02T18:47:27.783222Z","shell.execute_reply":"2021-08-02T18:49:03.246452Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1728x1152 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}